Back close
Admissions

Dr. Krishna Kumar P.

Associate Professor, Department of Mechanical Engineering, School of Engineering, Coimbatore

Qualification: M.E, Ph.D
p_kkumar@cb.amrita.edu
Research Interest: Design & Analysis, Metal Cutting, Micro Machining

Bio Publications
Bio

Krishna Kumar P. currently serves as Associate Professor & Dy.CoE at Department of Mechanical Engineering, School of Engineering, Coimbatore Campus. His areas of research include Design and Analysis, Metal Cutting and Micro Machining.

Educational Qualification

Degree Year of Completion Name of the Institution Name of the Board / University
Ph D 2017 Amrita School of Engineering, Coimbatore Amrita Vishwa Vidyapeetham
ME<pme< p=””></pme<> 2000 PSG College of Technology Bharathiar
BE 1998 Maharaja Engineering College Bharathiar

Experience

S.No

 

 Name of the Organisation

 

 Period of Service Designation

 
1 Amrita  School of Engineering 01/07/2010 to Present Associate Professor
2 Amrita  School of Engineering 01/ 07/2007 to 30/06/2010 Assistant Professor
3 Amrita School of Engineering 01/07 /2004 to  30/06/2007 Senior Lecturer
4 Amrita School of Engineering 02/02-2000 to 30 /06 /2004 Lecturer

Projects

S. No. Name of the Project Name of the Funding Agency Status Project Grant / Assistance (Rs.) Duration of the Project
Process Monitoring and Control of Ultra Precision Machining of Titanium alloys * DRDO Completed
June 2012		 14.00 Lakhs 2 years 3 months
2. Fault diagnosis of dynamic mechanical systems based on signal processing using machine learning techniques DRDO Completed
June 2015		 28.89 Lakhs 3 years
3 Investigations into the surface integrity of Ti alloys during high speed machining* AR&DB Completed Sept.  2016 Rs.9.06 Lakhs 2 Years
4 Cloud based hybrid pattern recognition approach for fault diagnosis of motor driven rotating machines using motor current signature analysis DRDO Submitted
October 2016		 R.30.90 Lakhs 3 Years

Seminars, Workshops and Conferences Organised

  • Workshop on Condition Monitoring- Welding and Machining and applications – 25th March 2015
  • Two day workshop on Recent Trends in Manufacturing – 27.03.2014 to 28.03.2014 – funded by ISRO
  • CNC Programming and Operations – 07.01.2013 to 11.01.2013
  • Two day workshop on Recent Trends in Machining – 25th and 26th August 2011 –funded by ISRO and DRDO
  • CNC Programming and Operations – 26.12.2011 to 30.12.2011
  • COSMA 2011 – 14.12.2011 to 16.12.2011 – served as a committee member
  • Actively participated in other International Conferences and Workshops Organized by the department in different committees like WCM.,etc.,

Present Responsibilities at Department Level

  • M.Tech Manufacturing Engineering – Program Coordinator
  • M.Tech Project Coordinator Lab In-charge
  • Special Machines BoS Member
  • M.Tech Manufacturing Engineering AUMS coordinator

Present Responsibilities at School Level

Deputy Controller of Examinations

Responsibilities Held

  • Time table in-charge – department level – 7 years
  • Time table coordinator – school level – 5 years
  • B.Tech admission – counselling – seat allotment coordinator – 4 years
  • B.Tech admission – Exhibition in-charge at various venues in Tamilnadu
  • AUMS committee member – school level
  • Department Library in-charge
  • Lab In charge- Lathe and Press
  • Shop Sports Day – Event In-charge
  • Float in-charge – Gokulastami – 2010

Ashram Activities

  • Amala Bharatham Cleaning Programme
  • Food Committee in charge during AMMA’s Birthday celebrations.
  • Crowd control – AMMA visits to Kovai.

Under Review

  • Krishnakumar, P., Rameshkumar K, Ramachandran, K.I., Machine learning based tool condition classification using AE and vibration data in a high speed milling process using wavelet features. Intelligent Decision Technologies: An International Journal, IOS, under review- submitted on 06-01-2017.
  • Krishnakumar, P., Rameshkumar K, Ramachandran, K.I., Acoustic emission based tool condition classification in a precision high speed machining of titanium alloy (Ti-6Al-4V): A machine learning approach. International Journal of Computational Intelligence and Applications, under review- submitted on 15-03-2017.
  • Krishnakumar, P., Rameshkumar K, Ramachandran, K.I., Feature level vibration and acoustic emission sensor signal fusion to improve classification efficiency in tool condition monitoring using machine learning classifiers. IJPHM, under review -submitted on 19-05-2017.
Publications

Journal Article

Year : 2020

Chatter Prediction in High Speed Machining of Titanium Alloy (Ti-6Al-4V) using Machine Learning Techniques

Cite this Research Publication : K. Zacharia and Krishna Kumar P., “Chatter Prediction in High Speed Machining of Titanium Alloy (Ti-6Al-4V) using Machine Learning Techniques”, Materials Today: Proceedings, vol. 24, pp. 350-358, 2020.

Publisher : Materials Today

Year : 2020

Hidden Markov Modelling of High-Speed Milling (HSM) Process Using Acoustic Emission (AE) Signature for Predicting Tool Conditions

Cite this Research Publication : S. P. Krishnan, K. Ramesh Kumar, and Krishna Kumar P., “Hidden Markov Modelling of High-Speed Milling (HSM) Process Using Acoustic Emission (AE) Signature for Predicting Tool Conditions”, Advances in Materials and Manufacturing Engineering, 2020.

Publisher : Advances in Materials and Manufacturing Engineering

Year : 2019

Fault Diagnosis of Gearbox Using Machine Learning and Deep Learning Techniques

Cite this Research Publication : T. Praveenkumar, Dr. Saimurugan M., Krishna Kumar P., and I, R. K., “Fault Diagnosis of Gearbox Using Machine Learning and Deep Learning Techniques”, International Journal of Engineering and Advanced Technology, vol. 9, pp. 3940-3943, 2019.


Publisher : International Journal of Engineering and Advanced Technology

Year : 2018

Feature level vibration and acoustic emission sensor signal fusion to improve classification efficiency in tool condition monitoring using machine learning classifiers

Cite this Research Publication : Krishna Kumar P., ,, and Ramachandran, K. I., “Feature level vibration and acoustic emission sensor signal fusion to improve classification efficiency in tool condition monitoring using machine learning classifiers”, International Journal of Prognostics and Health Management, vol. 9, no. 1, 2018.

Publisher : International Journal of Prognostics and Health Management,

Year : 2018

Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers

Cite this Research Publication : Krishna Kumar P., K. Ramesh Kumar, and Dr. K. I. Ramachandran, “Feature level fusion of vibration and acoustic emission signals in tool condition monitoring using machine learning classifiers”, International Journal of Prognostics and Health Management, vol. 9, no. 8, pp. 2153-2648, 2018.


Publisher : International Journal of Prognostics and Health Management, Prognostics and Health Management Society

Year : 2018

Machine Learning Based Tool Condition classification using AE and Vibration data in a High Speed Milling Process Using Wavelet Features

Cite this Research Publication : K. Ramesh Kumar, Krishna Kumar P., and Ramachandran, K. I., “Machine Learning Based Tool Condition classification using AE and Vibration data in a High Speed Milling Process Using Wavelet Features”, Intelligent Decision Technologies: An International Journal, 2018.

Publisher : Intelligent Decision Technologies: An International Journal

Year : 2018

Machine learning based tool condition classification using acoustic emission and vibration data in high speed milling process using wavelet features

Cite this Research Publication : P. Krishnakumar, K. Ramesh Kumar, and Dr. K. I. Ramachandran, “Machine learning based tool condition classification using acoustic emission and vibration data in high speed milling process using wavelet features”, Intelligent Decision Technologies, vol. 12, pp. 1-18, 2018.

Publisher : Intelligent Decision Technologies .

Year : 2018

Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach

Cite this Research Publication : P. Krishnakumar, K. Ramesh Kumar, and RAMACHANDRAN, K. I., “Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach”, International Journal of Computational Intelligence and Applications, vol. 17, p. 1850017, 2018.

Publisher : International Journal of Computational Intelligence and Applications, Volume 17, Number 03, p.1850017 (2018).

Year : 2018

Acoustic emission based tool condition classification in a precision high speed machining of titanium alloy (Ti-6Al-4V): A machine learning approach

Cite this Research Publication : Krishna Kumar P., K. Ramesh Kumar, and Dr. K. I. Ramachandran, “Acoustic emission based tool condition classification in a precision high speed machining of titanium alloy (Ti-6Al-4V): A machine learning approach”, International Journal of Computational Intelligence and Applications, vol. 17, 2018.

Publisher : International Journal of Computational Intelligence and Applications, World Scientific Publishing Co

Year : 2016

Finite Element Modelling and Residual Stress Prediction in End Milling of Ti6Al4Valloy

Cite this Research Publication : Krishna Kumar P., Sripathi, J., Vijay, P., and Dr. K. I. Ramachandran, “Finite Element Modelling and Residual Stress Prediction in End Milling of Ti6Al4Valloy”, IOP Conference Series: Materials Science and Engineering, vol. 149, p. 012154, 2016.

Publisher : IOP Conference Series: Materials Science and Engineering.

Year : 2015

A Study on Classification Ability of Decision Tress and Support Vector Machine in Gear Box Fault Detection

Cite this Research Publication : M. Saimurugan, Praveenkumar, T., and Krishna Kumar P., “A Study on Classification Ability of Decision Tress and Support Vector Machine in Gear Box Fault Detection”, Applied Mechanics and Materials, pp. 813-814, 2015.

Publisher : Applied Mechanics and Materials

Year : 2015

Tool Wear Condition Prediction Using Vibration Signals in High Speed Machining (HSM) of Titanium (Ti-6Al-4V) Alloy

Cite this Research Publication : Krishna Kumar P., Rameshkumar, K., and Dr. K. I. Ramachandran, “Tool Wear Condition Prediction Using Vibration Signals in High Speed Machining (HSM) of Titanium (Ti-6Al-4V) Alloy”, Procedia Computer Science, vol. 50, pp. 270 - 275, 2015.

Publisher : Procedia Computer Science

Year : 2015

A Study on the Classification Ability of Decision Tree and Support Vector Machine in Gearbox Fault Detection

Cite this Research Publication : Dr. Saimurugan M., T. Praveenkumar, Krishna Kumar P., and Ramachandran, K. I., “A Study on the Classification Ability of Decision Tree and Support Vector Machine in Gearbox Fault Detection”, Applied Mechanics and Materials, vol. 813-814, pp. 1058-1062, 2015.


Publisher : Applied Mechanics and Materials

Year : 2014

Fault Diagnosis of Automobile Gearbox Based on Machine Learning Techniques

Cite this Research Publication : T. Praveenkumar, Dr. Saimurugan M., Krishna Kumar P., and Dr. K. I. Ramachandran, “Fault Diagnosis of Automobile Gearbox Based on Machine Learning Techniques”, Procedia Engineering, vol. 97, pp. 2092–2098, 2014.

Publisher : Procedia Engineering

Year : 2013

Finite element simulation of effect of residual stresses during orthogonal machining using ALE

Cite this Research Publication : Prakash Marimuthu K., Krishna Kumar P., K. Ramesh Kumar, and Dr. K. I. Ramachandran, “Finite element simulation of effect of residual stresses during orthogonal machining using ALE approach”, International Journal of Machining and Machinability of Materials, vol. 14, pp. 213–229, 2013.

Publisher : International Journal of Machining and Machinability of Materials, Inderscience

Year : 2012

Productivity Improvement of a Manufacturing Enterprise using Lean Tools: A Case Study in Discrete Manufacturing Sector

Cite this Research Publication : K. Rameshkumar, Sumesh, A., Krishna Kumar P., and T, C. Austin V., “Productivity Improvement of a Manufacturing Enterprise using Lean Tools: A Case Study in Discrete Manufacturing Sector”, Indore Management Journal, vol. 3, no. 2, pp. 34-4, 2012.

Publisher : Indore Management Journal

Conference Paper

Year : 2008

Simulation optimization in a Kanban Controlled Flowshop

Cite this Research Publication : A. Sumesh, K. Ramesh Kumar, and Krishna Kumar P., “Simulation Optimization in a Kanban Controlled Flow Shop”, in ORSI – 2008/TIRUPATHI, 2008.


Publisher : ORSI – 2008/TIRUPATHI, 2008.

Year : 2008

Simulation optimization in a kanban controlled flow shop

Cite this Research Publication : A. Sumesh, K. Ramesh Kumar, and Krishna Kumar P., “Simulation Optimization in a Kanban Controlled Flow Shop”, in ORSI – 2008/TIRUPATHI, 2008.

Publisher : ORSI

Conference Proceedings

Year : 2013

Finite Element Modelling of Residual Stress in High Speed Machining of Titanium Alloy

Cite this Research Publication : Krishna Kumar P., Vishnu, J., Ramachandran, K. I. ., and Rameshkumar, K., “Finite Element Modelling of Residual Stress in High Speed Machining of Titanium Alloy”, CAE international conference, IIT, Chennai. 2013.

Publisher : CAE international conference, IIT, Chennai

Year : 2012

Vibration based Tool Condition Monitoring (TCM) in machining of Titanium alloy (Ti-6Al-4V) using machine Learning Algorithms

Cite this Research Publication : Krishna Kumar P., K., R., and Ramachandran, K. I., “Vibration based Tool Condition Monitoring (TCM) in machining of Titanium alloy (Ti-6Al-4V) using machine Learning Algorithms”, International Conference on Optimization, Computing & Business Analytics (ICOCBA 2012).- ORSI International Conference. 2012.

Publisher : ICOCBA 2012

Year : 2010

Productivity Improvement of a Manufacturing Industry Using Value Stream Mapping (VSM) Approach: A case Study in a Discrete Ma1nufacturing Sector

Cite this Research Publication : K. Ramesh Kumar, Krishna Kumar P., and A. Sumesh, “Productivity Improvement of a Manufacturing Industry Using Value Stream Mapping (VSM) Approach: A case Study in a Discrete Ma1nufacturing Sector”, International Conference on operational research for urban and rural development (ORURD). 2010.


Publisher : ORURD

Year : 2009

End milling process parameter optimization using Particle Swarm Optimization Algorithms

Cite this Research Publication : Krishna Kumar P. and K, R., “End milling process parameter optimization using Particle Swarm Optimization Algorithms”, International conference on Emerging Research and Advances in Mechanical Engineering. Velammal Engineering College, Chennai., 2009.

Publisher : International conference on Emerging Research and Advances in Mechanical Engineering

Year : 2009

Finite Difference Modelling of Laser Drilling for Machining Silicon Carbide

Cite this Research Publication : Krishna Kumar P. and K, R., “Finite Difference Modelling of Laser Drilling for Machining Silicon Carbide”. National Engineering College, Kovilpatti., 2009.

Publisher : National Engineering College

Admissions Apply Now